





"""simple docstring"""





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Any     ):







      '''simple docstring'''




      lowerCAmelCase    		=			"""huggingface/label-files"""
      lowerCAmelCase    		=			"""imagenet-1k-id2label.json"""
      lowerCAmelCase    		=			json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		repo_type="""dataset"""     )				,		"""r"""     )     )
      lowerCAmelCase    		=			{int(SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}
      lowerCAmelCase    		=			{v: k for k, v in idalabel.items()}

      lowerCAmelCase    		=			"""std_conv""" if """bit""" in model_name else False

      # note that when using BiT as backbone for ViT-hybrid checkpoints,
      # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
      # config.conv_layer = "std_conv_same"
      lowerCAmelCase    		=			BitConfig(
          conv_layer=SCREAMING_SNAKE_CASE				,		num_labels=10_00				,		idalabel=SCREAMING_SNAKE_CASE				,		labelaid=SCREAMING_SNAKE_CASE				,		)

      return config


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      if "stem.conv" in name:
            lowerCAmelCase    		=			name.replace("""stem.conv"""				,		"""bit.embedder.convolution"""     )
      if "blocks" in name:
            lowerCAmelCase    		=			name.replace("""blocks"""				,		"""layers"""     )
      if "head.fc" in name:
            lowerCAmelCase    		=			name.replace("""head.fc"""				,		"""classifier.1"""     )
      if name.startswith("""norm"""     ):
            lowerCAmelCase    		=			"""bit.""" + name
      if "bit" not in name and "classifier" not in name:
            lowerCAmelCase    		=			"""bit.encoder.""" + name

      return name


def      UpperCAmelCase__				(    ):







      '''simple docstring'''




      lowerCAmelCase    		=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      lowerCAmelCase    		=			Image.open(requests.get(SCREAMING_SNAKE_CASE				,		stream=SCREAMING_SNAKE_CASE     ).raw     )
      return im


@torch.no_grad()
def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[int]				,		SCREAMING_SNAKE_CASE	:		Tuple				,		SCREAMING_SNAKE_CASE	:		Optional[int]=False     ):







      '''simple docstring'''




      lowerCAmelCase    		=			get_config(SCREAMING_SNAKE_CASE     )

      # load original model from timm
      lowerCAmelCase    		=			create_model(SCREAMING_SNAKE_CASE				,		pretrained=SCREAMING_SNAKE_CASE     )
      timm_model.eval()

      # load state_dict of original model
      lowerCAmelCase    		=			timm_model.state_dict()
      for key in state_dict.copy().keys():
            lowerCAmelCase    		=			state_dict.pop(SCREAMING_SNAKE_CASE     )
            lowerCAmelCase    		=			val.squeeze() if """head""" in key else val

      # load HuggingFace model
      lowerCAmelCase    		=			BitForImageClassification(SCREAMING_SNAKE_CASE     )
      model.eval()
      model.load_state_dict(SCREAMING_SNAKE_CASE     )

      # create image processor
      lowerCAmelCase    		=			create_transform(**resolve_data_config({}				,		model=SCREAMING_SNAKE_CASE     )     )
      lowerCAmelCase    		=			transform.transforms

      lowerCAmelCase    		=			{
          """bilinear""": PILImageResampling.BILINEAR,
          """bicubic""": PILImageResampling.BICUBIC,
          """nearest""": PILImageResampling.NEAREST,
      }

      lowerCAmelCase    		=			BitImageProcessor(
          do_resize=SCREAMING_SNAKE_CASE				,		size={"""shortest_edge""": timm_transforms[0].size}				,		resample=pillow_resamplings[timm_transforms[0].interpolation.value]				,		do_center_crop=SCREAMING_SNAKE_CASE				,		crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}				,		do_normalize=SCREAMING_SNAKE_CASE				,		image_mean=timm_transforms[-1].mean.tolist()				,		image_std=timm_transforms[-1].std.tolist()				,		)

      lowerCAmelCase    		=			prepare_img()
      lowerCAmelCase    		=			transform(SCREAMING_SNAKE_CASE     ).unsqueeze(0     )
      lowerCAmelCase    		=			processor(SCREAMING_SNAKE_CASE				,		return_tensors="""pt"""     ).pixel_values

      # verify pixel values
      assert torch.allclose(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )

      # verify logits
      with torch.no_grad():
            lowerCAmelCase    		=			model(SCREAMING_SNAKE_CASE     )
            lowerCAmelCase    		=			outputs.logits

      print("""Logits:"""				,		logits[0, :3]     )
      print("""Predicted class:"""				,		model.config.idalabel[logits.argmax(-1     ).item()]     )
      lowerCAmelCase    		=			timm_model(SCREAMING_SNAKE_CASE     )
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(SCREAMING_SNAKE_CASE				,		outputs.logits				,		atol=1e-3     )
      print("""Looks ok!"""     )

      if pytorch_dump_folder_path is not None:
            Path(SCREAMING_SNAKE_CASE     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE     )
            print(F'Saving model {model_name} and processor to {pytorch_dump_folder_path}'     )
            model.save_pretrained(SCREAMING_SNAKE_CASE     )
            processor.save_pretrained(SCREAMING_SNAKE_CASE     )

      if push_to_hub:
            print(F'Pushing model {model_name} and processor to the hub'     )
            model.push_to_hub(F'ybelkada/{model_name}'     )
            processor.push_to_hub(F'ybelkada/{model_name}'     )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default="resnetv2_50x1_bitm",
           type=str,
           help="Name of the BiT timm model you'd like to convert.",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
       )
       parser.add_argument(
           "--push_to_hub",
           action="store_true",
           help="Whether to push the model to the hub.",
       )

       SCREAMING_SNAKE_CASE__   =  parser.parse_args()
       convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)













"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__   =  {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  ["RoFormerTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "RoFormerForCausalLM",
           "RoFormerForMaskedLM",
           "RoFormerForMultipleChoice",
           "RoFormerForQuestionAnswering",
           "RoFormerForSequenceClassification",
           "RoFormerForTokenClassification",
           "RoFormerLayer",
           "RoFormerModel",
           "RoFormerPreTrainedModel",
           "load_tf_weights_in_roformer",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFRoFormerForCausalLM",
           "TFRoFormerForMaskedLM",
           "TFRoFormerForMultipleChoice",
           "TFRoFormerForQuestionAnswering",
           "TFRoFormerForSequenceClassification",
           "TFRoFormerForTokenClassification",
           "TFRoFormerLayer",
           "TFRoFormerModel",
           "TFRoFormerPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "FlaxRoFormerForMaskedLM",
           "FlaxRoFormerForMultipleChoice",
           "FlaxRoFormerForQuestionAnswering",
           "FlaxRoFormerForSequenceClassification",
           "FlaxRoFormerForTokenClassification",
           "FlaxRoFormerModel",
           "FlaxRoFormerPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
       from .tokenization_roformer import RoFormerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roformer_fast import RoFormerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roformer import (
                  ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RoFormerForCausalLM,
                  RoFormerForMaskedLM,
                  RoFormerForMultipleChoice,
                  RoFormerForQuestionAnswering,
                  RoFormerForSequenceClassification,
                  RoFormerForTokenClassification,
                  RoFormerLayer,
                  RoFormerModel,
                  RoFormerPreTrainedModel,
                  load_tf_weights_in_roformer,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roformer import (
                  TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRoFormerForCausalLM,
                  TFRoFormerForMaskedLM,
                  TFRoFormerForMultipleChoice,
                  TFRoFormerForQuestionAnswering,
                  TFRoFormerForSequenceClassification,
                  TFRoFormerForTokenClassification,
                  TFRoFormerLayer,
                  TFRoFormerModel,
                  TFRoFormerPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roformer import (
                  FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaxRoFormerForMaskedLM,
                  FlaxRoFormerForMultipleChoice,
                  FlaxRoFormerForQuestionAnswering,
                  FlaxRoFormerForSequenceClassification,
                  FlaxRoFormerForTokenClassification,
                  FlaxRoFormerModel,
                  FlaxRoFormerPreTrainedModel,
              )


else:
       import sys

       SCREAMING_SNAKE_CASE__   =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






